{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T15:59:23Z","timestamp":1790611163346,"version":"4.1.0"},"posted":{"date-parts":[[2026]]},"group-title":"SSRN","reference-count":0,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.uspto.gov\/ip-policy\/copyright-policy\/copyright-basics"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>Reliable prediction of Economic Policy Uncertainty (EPU) and Geopolitical Risk (GPRC) is crucialfor understanding economic activity, financial markets, and broader macroeconomic dynamics.However, forecasting these series remains challenging due to their high volatility, long-memorydependence, and nonlinear behavior. This study addresses these challenges by proposing a fractionallydifferenced Bayesian neural network (fBNN) framework that jointly captures nonlineardynamics and long-memory structures. The proposed model incorporates macroeconomic variablesand Google Trends indicators to reflect both economic-financial conditions and public attention.Empirical results demonstrate the consistent superiority of fBNN over state-of-the-art statisticaland machine learning benchmarks in forecasting EPU and GPRC indices. The Bayesian frameworkfurther provides credible intervals, enhancing model interpretability by quantifying forecast uncertainty.Overall, the study offers useful insights for policymakers and market participants operatingin uncertain and volatile economic environments.<\/jats:p>","DOI":"10.2139\/ssrn.7537189","type":"posted-content","created":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T15:44:59Z","timestamp":1790610299000},"source":"Crossref","is-referenced-by-count":0,"title":["Forecasting Policy Uncertainty with FractionallyDifferenced Bayesian Neural Networks"],"prefix":"10.2139","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9338-9914","authenticated-orcid":true,"given":"Muhammed  Saheer","family":"Madathil","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammed  Navas","family":"T","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","container-title":[],"original-title":[],"deposited":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T15:44:59Z","timestamp":1790610299000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ssrn.com\/abstract=7537189"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":0,"URL":"https:\/\/doi.org\/10.2139\/ssrn.7537189","relation":{},"subject":[],"published":{"date-parts":[[2026]]},"subtype":"preprint"}}